MétaCan
Menu
Back to cohort
Record W7053958167

자아 정체성, 국가와 성의 정치학: - 마거릿 앳우드의『수면 위로 떠오르기』 = Self-Identity, the Politics of Nation and Gender: Margaret Atwood’s <i>Surfacing</i>

2017· article· ko· W7053958167 on OpenAlexaboutno aff

Bibliographic record

VenueProject Muse (Johns Hopkins University) · 2017
Typearticle
Languageko
FieldEngineering
TopicLaser Design and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsMythologyOrder (exchange)Identity (music)Stereotype (UML)Presupposition
DOInot available

Abstract

fetched live from OpenAlex

In <i>Surfacing</i>, Margaret Atwood, who is one of the most powerful writers in Canada, creates a nameless speaker and heroine in order to explore her identity as a woman and in a macro sense, as a nation. She accomplishes the whole process by the technique of using visual devices such as albums, pictures, video cameras, illustrations, and images in order to create a tool to use for the speaker’s job as an illustrator. First, she makes her heroine revise Canadian cultural myths and the official history of Canadian former settlers and re-evaluate all cultural assumptions and presuppositions on Canada and women. And she then causes her heroine to enter upon a quest for her self-identity, which has been fixed with in the stereotype of Western fashions, especially when it has related to the politics of the nation and the female gender. This indicates Atwood’s self-criticism of Canada and Canadians, in her hope to remake Canada as a nation, a culture, and a society and to help individuals, such as women, to find their proper identities, survive their attributed selves, and live their independent lives. Therefore, I can say that <i>Surfacing</i> is Atwood’s expression of love for Canada and of her dream for a hopeful future for all Canadians.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.934
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.230
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueProject Muse (Johns Hopkins University)Same topicLaser Design and ApplicationsFrench-language works237,207